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Two-microphone separation of speech mixtures.

Michael Syskind Pedersen1, DeLiang Wang, Jan Larsen

  • 1Oticon A/S, Smørum DK-2765, Denmark. msp@oticon.dk

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This study introduces a new method for separating mixed speech signals, even with fewer microphones than speech sources. The technique combines independent component analysis (ICA) and time-frequency masking to isolate multiple audio streams effectively.

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Area of Science:

  • Signal Processing
  • Acoustics
  • Machine Learning

Background:

  • The "cocktail party problem" involves separating mixed audio signals.
  • Existing methods often require more microphones than sources or prior knowledge of the number of sources.
  • These limitations hinder real-world applications of source separation.

Purpose of the Study:

  • To develop a novel method for underdetermined blind source separation.
  • To overcome limitations of existing methods in terms of sensor and source count.
  • To enable effective speech separation in challenging acoustic environments.

Main Methods:

  • Combines independent component analysis (ICA) with binary time-frequency (T-F) masking.
  • Employs an iterative approach to extract and refine basis speech signals from convolutive mixtures.
  • Utilizes closely spaced microphones for an instantaneous mixing model.

Main Results:

  • Successfully separates an arbitrary number of mixed speech signals using only two microphones.
  • Demonstrates effective separation for mixtures with up to seven speech signals under instantaneous conditions.
  • Shows applicability to segregating speech signals in reverberant environments.

Conclusions:

  • The proposed method overcomes traditional limitations in underdetermined blind source separation.
  • It allows for the separation of multiple speech signals without prior knowledge of their count.
  • The technique offers a robust solution for real-world audio separation challenges, maintaining stereo output.